Cooperative Communications for Wireless Ad Hoc and Sensor Networks in 2013Guest Editors: Yong Sun, Shukui Zhang, Hongli Xu, and Shan Lin

International Journal of Distributed Sensor Networks

Cooperative Communications for WirelessAd Hoc and Sensor Networks in 2013

International Journal of Distributed Sensor Networks

Cooperative Communications for WirelessAd Hoc and Sensor Networks in 2013

Guest Editors: Yong Sun, Shukui Zhang, Hongli Xu,and Shan Lin

Copyright © 2014 Hindawi Publishing Corporation. All rights reserved.

This is a special issue published in “International Journal of Distributed Sensor Networks.” All articles are open access articles distributedunder the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, pro-vided the original work is properly cited.

Editorial Board

Miguel Acevedo, USASanghyun Ahn, KoreaMohammod Ali, USAJamal N. Al-Karaki, JordanHabib M. Ammari, USAJavier Bajo, SpainPrabir Barooah, USAAlessandro Bogliolo, ItalyRichard R. Brooks, USAJames Brusey, UKErik Buchmann, GermanyJian-Nong Cao, Hong KongJoão Paulo Carmo, PortugalJesús Carretero, SpainLuca Catarinucci, ItalyHenry Chan, Hong KongChih-Yung Chang, TaiwanPeriklis Chatzimisios, GreeceAi Chen, ChinaPeng Cheng, ChinaJinsung Cho, KoreaKim-Kwang R. Choo, AustraliaChi-Yin Chow, Hong KongWan-Young Chung, KoreaMauro Conti, ItalyDinesh Datla, USAAmitava Datta, AustraliaDanilo De Donno, ItalyIlker Demirkol, SpainDer-Jiunn Deng, TaiwanChyi-Ren Dow, TaiwanGeorge P. Efthymoglou, GreeceFrank Ehlers, ItalyMelike Erol-Kantarci, CanadaGiancarlo Fortino, ItalyLuca Foschini, ItalyDavid Galindo, FranceWeihua Gao, USADeyun Gao, ChinaAthanasios Gkelias, UKIqbal Gondal, AustraliaJayavardhana Gubbi, AustraliaCagri Gungor, TurkeySong Guo, JapanAndrei Gurtov, Finland

Qi Han, USAZ. Hanzalek, Czech RepublicTian He, USAJunyoung Heo, KoreaZujun Hou, SingaporeBaoqi Huang, ChinaChin-Tser Huang, USAYung-Fa Huang, TaiwanXinming Huang, USAJiun-Long Huang, TaiwanWei Huangfu, ChinaMohamed Ibnkahla, CanadaTan Jindong, USAIbrahim Kamel, UAELi-Wei Kang, TaiwanRajgopal Kannan, USASherif Khattab, EgyptLisimachos Kondi, GreeceMarwan Krunz, USAKun-Chan Lan, TaiwanYee W. Law, AustraliaYoung-Koo Lee, KoreaKyung-Chang Lee, KoreaYong Lee, USAJongHyup Lee, KoreaSungyoung Lee, KoreaSeokcheon Lee, USAJoo-Ho Lee, JapanShijian Li, ChinaMinglu Li, ChinaShuai Li, USAShancang Li, UKYe Li, ChinaZhen Li, ChinaYao Liang, USAJing Liang, ChinaWeifa Liang, AustraliaWen-Hwa Liao, TaiwanAlvin S. Lim, USAKai Lin, ChinaZhong Liu, ChinaMing Liu, ChinaDonggang Liu, USAYonghe Liu, USAZhigang Liu, China

Chuan-Ming Liu, TaiwanLeonardo Lizzi, FranceGiuseppe Lo Re, ItalySeng Loke, AustraliaJonathan Loo, UKJuan Antonio López Riquelme, SpainPascal Lorenz, FranceKingShan Lui, Hong KongJun Luo, SingaporeJose Ramiro Martinez-de Dios, SpainNirvana Meratnia, The NetherlandsShabbir N. Merchant, IndiaMihael Mohorcic, SloveniaJosé Molina, SpainV. Muthukkumarasamy, AustraliaEduardo Freire Nakamura, BrazilKamesh Namuduri, USAGeorge Nikolakopoulos, SwedenMarimuthu Palaniswami, AustraliaAi-Chun Pang, TaiwanSeung-Jong J. Park, USASoo-Hyun Park, KoreaMiguel A. Patricio, SpainWen-Chih Peng, TaiwanJanez Per, SloveniaDirk Pesch, IrelandShashi Phoha, USAAntonio Puliafito, ItalyHairong Qi, USANageswara S.V. Rao, USAMd. Abdur Razzaque, BangladeshPedro Pereira Rodrigues, PortugalJoel J. P. C. Rodrigues, PortugalJorge Sa Silva, PortugalMohamed Saad, UAESanat Sarangi, IndiaStefano Savazzi, ItalyMarco Scarpa, ItalyArunabha Sen, USAXiao-Jing Shen, ChinaWeihua Sheng, USALouis Shue, SingaporeAntonino Staiano, ItalyTan-Hsu Tan, TaiwanGuozhen Tan, China

Shaojie Tang, USABulent Tavli, TurkeyAnthony Tzes, GreeceAgustinus B. Waluyo, AustraliaYu Wang, USARan Wolff, IsraelJianshe Wu, ChinaWen-Jong Wu, TaiwanChase Qishi Wu, USA

Bin Xiao, Hong KongQin Xin, Faroe IslandsJianliang Xu, Hong KongYuan Xue, USATing Yang, ChinaHong-Hsu Yen, TaiwanLi-Hsing Yen, TaiwanSeong-eun Yoo, KoreaNing Yu, China

Changyuan Yu, SingaporeTianle Zhang, ChinaYanmin Zhu, ChinaT. L. Zhu, USAYi-hua Zhu, ChinaQingxin Zhu, ChinaLi Zhuo, ChinaShihong Zou, China

Contents

Cooperative Communications for Wireless Ad Hoc and Sensor Networks in 2013, Yong Sun,Shukui Zhang, Hongli Xu, and Shan LinVolume 2014, Article ID 274378, 2 pages

Spectrum Leasing in Cognitive Radio Networks: A Survey, Aqeel Raza Syed and Kok-Lim Alvin YauVolume 2014, Article ID 329235, 22 pages

Partial Relay Selection with Feedback Delay and Cochannel Interference: Performance Analysis andPower Optimization, Xuanxuan Tang, WeifengMou, Fang Fang, Yueming Cai, Weiwei Yang, and Tao ZhangVolume 2014, Article ID 629348, 6 pages

Cognitive Wireless Sensor Network Platform for Cooperative Communications, Agust́ın Tena,Guillermo Jara, Juan Domingo, Elena Romero, and Alvaro AraujoVolume 2014, Article ID 473905, 8 pages

Improved Message Diffusion Model for Node Coverage Problem of Ad Hoc Network Based on NodeVisit Times, Zhe Yang, Lingzhi Li, Shukui Zhang, Yong Sun, and Yanqin ZhuVolume 2013, Article ID 264383, 11 pages

Distributed Testbed for Coded Cooperation with Software-Defined Radios, Changcai Han and Si LiVolume 2013, Article ID 325301, 9 pages

Energy Efficient Power Allocation for Bidirectional Relaying with Imperfect Channel Estimation,Song Li, Yanjing Sun, Hongli Xu, Zhi Sun, and Wenjuan ShiVolume 2013, Article ID 725459, 8 pages

Hierarchical Spatial Clustering in MultihopWireless Sensor Networks, Zhidan Liu, Wei Xing,Yongchao Wang, and Dongming LuVolume 2013, Article ID 528980, 11 pages

Joint Relay Ordering and Linear Finite Field Network Coding for Multiple-Source Multiple-RelayWireless Sensor Networks, Yulun Cheng and Longxiang YangVolume 2013, Article ID 729869, 9 pages

Application of Baro-Altimeter Sensor in Emergency Positioning and Navigation Based on CompassGEO Satellites, Jianwei Zhan, Jing Pang, Guozhu Zhang, and Gang OuVolume 2013, Article ID 974132, 18 pages

Throughput-Optimal Scheduling for Cooperative Communications inWireless Ad Hoc Networks,Thong Huynh, Won-Joo Hwang, and Suk-Hwan LeeVolume 2013, Article ID 376028, 10 pages

A Service Model for 6LoWPANWireless Sensor Networks, Xiaonan Wang and Haili HuangVolume 2013, Article ID 692735, 8 pages

Hindawi Publishing CorporationInternational Journal of Distributed Sensor NetworksVolume 2014, Article ID 274378, 2 pageshttp://dx.doi.org/10.1155/2014/274378

EditorialCooperative Communications for Wireless Ad Hoc andSensor Networks in 2013

Yong Sun,1 Shukui Zhang,1 Hongli Xu,2 and Shan Lin3

1 Computer Science and Technology Institute, Soochow University, Suzhou, Jiangsu 215006, China2 School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui 215011, China3Department of Computer and Information Sciences, Temple University, 324 Wachman Hall, 1805 North Broad Street,Philadelphia, PA, USA

Correspondence should be addressed to Yong Sun; suny@suda.edu.cn

Received 29 December 2013; Accepted 29 December 2013; Published 19 March 2014

Copyright © 2014 Yong Sun et al.This is an open access article distributed under theCreative CommonsAttribution License, whichpermits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The concept of cooperative communications for wireless adhoc and sensor networks (WAHSNs) has recently attractedconsiderable attention. Being different from conventionalpoint-to-point communications, cooperative communica-tions allow different users or nodes in a WAHSN to shareresources to create collaboration through distributed trans-mission. It realizes a new form of spatial diversity to combatthe effects of severe fading; thus, it can be used widely inWAHSNs for the sake of resource minimization. However,the impact and challenges of cooperative communication inWAHSNs are not well understood yet. Some fundamentalaspects requiring immediate studies include theoretical toolsfor cooperative networks, effective incentive mechanismsfor cooperation, and new protocol design for cooperativenetworks. This issue tries to collect cutting-edge researchachievements in this special area.

The paper entitled “Improved message diffusion model fornode coverage problem of ad hoc network based on node visittimes,” by Z. Yang et al., analyzes the causes of the inaccuracyproblems of random samplingmodel and solves the problemsby specially introducing the factors such as node degreeand visit times. As for the E-R random network topology, itvalidates the effectiveness of the model proposed herein incontrast to the simulation experiment results.

In the paper entitled “Distributed testbed for coded coop-eration with software-defined radios,” by C. Han and S. Li,the authors design and implement a distributed hardwaretestbed using software-defined radios for cooperative com-munication, and the performance of two coded cooperation

schemes with turbo codes is evaluated in the physical layer.Furthermore, a distributed node synchronization scheme isimplemented and the source node and relay node work in thetime division protocol without any centralized controlling.

The paper entitled “Energy efficient power allocation forbidirectional relaying with imperfect channel estimation,” byS. Li et al., investigates the power allocation problem tominimize the total transmit power subject to constraints ontwo source nodes’ received signal-to-noise-ratios (SNRs).Thebest relay that minimizes the total transmit power is selectedin a multiple relay network.They also present outage analysiswhen the proposed power allocation is adopted and a close-form approximation of outage probability is obtained byshrinking the integral interval.

The paper entitled “Hierarchical spatial clustering in mul-tihop wireless sensor networks,” by Z. Liu et al., considers theproblem of spatial clustering for approximate data collectionthat is feasible and energy efficient for environment monitor-ing applications. They propose a clustering algorithm namedHSC to group the most similar sensor nodes in a distributedway. HSC runs on a prebuilt data collection tree and thusgets rid of some extra requirements such as global networktopology information and rigorous time synchronization.

The paper entitled “Joint relay ordering and linear finitefield network coding for multiple-source multiple-relay wirelesssensor networks,” by Y. Cheng and L. Yang, proposes a relayordering algorithm based on finite field NC (FFNC). In thescheme, the relays who initially fail to decode from sourcesare kept listening and searching for the opportunity to decode

2 International Journal of Distributed Sensor Networks

the signals from other relays, so as to recover the failure links.Moreover, the scheme is proved to own the merit of diffusioneffect, which makes the diversity improvement more efficientby simply increasing the relay number in the network.

The paper entitled “Application of baro-altimeter sensor inemergency positioning and navigation based on compass GEOsatellites,” by J. Zhan et al., presents a low-cost high-resolutionMS5534B barometric (baro-) module which applies to BDS.Firstly, the principle of emergency positioning based on abaro-altimeter sensor and its performance such as the accu-racy are elaborated. Then the effects of baro-altimeter sensormeasurement error on positioning are analyzed. Finally, afteranalyzing the limitation of the conventional algorithms, anew high-accuracy emergency positioning algorithm withbaro-altimeter sensor aiding is proposed, which is not limitedby the integration and user’s altitude.

The paper entitled “Throughput-optimal scheduling forcooperative communications in wireless ad hoc networks,” by T.Huynh et al., introduces the relay selection schemes that cancontrol the interference at the relay to prevent the relay thatmay harm other pairs. Then, they propose the throughput-optimal scheduling that takes into account error probabilityin decision andmaximizes throughput, that is, the amount ofpackets transmitted without error in network.

The paper entitled “A servicemodel for 6LoWPANwirelesssensor networks,” by X. Wang and H. Huang, proposesa 6LoWPAN service model based on the IPv6-based k-Anycast communication model. This model is extended into6LoWPAN service model so that the data-centric servicesof WSN can be achieved efficiently in the address-centric6LoWPAN.

Yong SunShukui Zhang

Hongli XuShan Lin

Review ArticleSpectrum Leasing in Cognitive Radio Networks: A Survey

Aqeel Raza Syed and Kok-Lim Alvin Yau

Department of Computer Science and Networked System, Sunway University, No. 5 Jalan Universiti, Bandar Sunway,46150 Petaling Jaya, Selangor, Malaysia

Correspondence should be addressed to Aqeel Raza Syed; 12010591@imail.sunway.edu.my

Received 8 July 2013; Accepted 6 December 2013; Published 13 February 2014

Academic Editor: Shukui Zhang

Copyright © 2014 A. R. Syed and K.-L. A.- Yau.This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited.

Cognitive Radio (CR) is a dynamic spectrum access approach, in which unlicensed users (or secondary users, SUs) exploit theunderutilized channels (or white spaces) owned by the licensed users (or primary users, PUs). Traditionally, SUs are oblivious toPUs, and therefore the acquisition of white spaces is not guaranteed. Hence, a SUmust vacate its channel whenever a PU reappearson it in an unpredictablemanner, whichmay affect the SUs’ network performance. Spectrum leasing has been proposed to tackle theaforementioned problem through negotiation between the PU and SU networks, which allows the SUs to acquire white spaces for aguaranteed period of time.Through spectrum leasing, the PUs and SUs enhance their network performances, and additionally PUsmaximize their respective monetary gains. Numerous research efforts have been made to investigate the CR, whereas the researchinto spectrum leasing remains at its infancy. In this paper, we present a comprehensive review on spectrum leasing schemes inCR networks by highlighting some pioneering approaches and discuss the gains, functionalities, characteristics, and challenges ofspectrum leasing schemes along with the performance enhancement in CR networks. Additionally, we discuss various open issuesin order to spark new interests in this research area.

1. Introduction

Cognitive Radio (CR) network, which is the next-generationwireless network, aims to improve the efficiency of spectrumutilization through dynamic spectrum access. There aretwo categories of users, namely, primary users (PUs) andsecondary users (SUs). Traditionally in CR networks, thePUs are the licensed users, and they have exclusive right touse their respective channels, while SUs are the unlicensedusers, and they use the underutilized channels (or whitespaces) opportunistically whenever PUs are not transmittingany packets. Hence, PUs are oblivious of the presence of SUs.There are twomain challenges associated with the traditionalCR Networks (CRNs) that adopt the opportunistic channelaccess approach. Firstly, the unpredictable PUs’ activitiesat any given time can significantly degrade SUs’ networkperformance (e.g., throughput and end-to-end delay) [1–4].Secondly, channel sensing [1], which is one of the main func-tions in the traditional CRNs, may require SUs to exchangechannel sensing outcomes among themselves, and this incurs

high amount of communication overhead resulting in higherenergy consumption and packet latency [5]. In addition to thetraditional CRNs [2, 3], there have been research activitiesin the area of CR sensor networks [4]. CR sensor networksare the next-generation wireless sensor networks that exploitwhite spaces through dynamic spectrum access.

Spectrum leasing is a dynamic spectrum access techniquein which PUs and SUs form a partnership formutual benefits.In spectrum leasing, the SUs negotiate with PUs and acquiretheir white spaces [6], while the PUs lease their channels andreceive rewards in the form ofmonetary gain or network per-formance enhancement through packet forwarding by SUs[7].Hence, PUs are fully aware of the presence of SUs. Figure 1presents a taxonomy of spectrum leasing, which covers itsadvantages, functionalities, characteristics, and challenges.Further descriptions about the taxonomy are found in the restof this section, as well as Sections 2, 3, and 4, respectively.Generally speaking, with the use of spectrum leasing, PUsand SUs receive the following advantages represented by (A1)and (A2) (see Figure 1), respectively.

Hindawi Publishing CorporationInternational Journal of Distributed Sensor NetworksVolume 2014, Article ID 329235, 22 pageshttp://dx.doi.org/10.1155/2014/329235

http://dx.doi.org/10.1155/2014/329235

2 International Journal of Distributed Sensor Networks

(F1.1)Determination of the cost of white spaces

(F1.2)Determination

of PU’s and SU’s channel access time

(F1.3) Relay selection

(F1.4)

transmission

(F2.1)Collaborator

selection

(F2.2)Determination

of SU’s channel access

time

(F2.3)SU’s packet transmission

(H1)Enhancing PU’s and

SU’s network performance

(H2)Inferring the channel characteristics of SU

relay nodes (H3)

Continuous

white spaces beingleased to SUs by PUs

(F1)PU’s functions

(F2)SU’s functions

(C2)Intracooperative

mode

(C3)Intercooperative

mode

(C1)Networking

topology

A Gains

FFunctions

CCharacteristics

HChallenges

Spectrum leasing

(A1)PU’s gain

(A2)SU’s gain

(A1.2)Network

performanceenhancement

(A1.1)Monetary

(A2.1)Dedicated

channel access(C1.1)

Centralized

(C1.2)Distributed

(C2.1)Intraco-

operative

(C2.2)Nonintraco- operative

(C3.1)Interco-

operative

(C3.2)Noninterco- operative

(H1.1)Increasing the amount of white spaces being

leased by PUs

(H1.2)Selecting an optimal channel with white

spaces by SUs

monitoring of

PU’s packet

gain

Figure 1: Taxonomy of spectrum leasing in CRNs.

(A1) PU’s Gain

(A1.1) Monetary Gain. PUsmay lease its licensed chan-nels during idle periods for financial rewardor revenue. For instance, Jayaweera et al. [6]propose a PU’s utility function based on itsmonetary gain (e.g., the price set by PUs ofwhitespaces).

(A1.2) Network Performance Enhancement. The PUlinks may deteriorate due to shadowing andinterference. Through spectrum leasing, one ormore SUs form an alternative route and relayPUs’ traffic, and this enhances the PUs’ networkperformance, such as successful transmissionrate, throughput, end-to-end delay, and energyefficiency [8].

(A2.1) Dedicated Channel Access. The SUs access whitespaces allocated by PUs. Subsequently, thisenhances the SUs’ throughput performance.Since spectrum leasing enhances the through-put performance of PUs (A1.2), it reduces thetransmission time of PUs, therefore leavingmore white spaces and transmission opportuni-ties to SUs for dedicated access [9].

The advantages motivate PUs and SUs to participatein spectrum leasing. For instance, in [5], spectrum leasingmaximizes a weighted sum of PUs’ and SUs’ throughputperformance.

This paper provides an extensive survey on existingspectrum leasing schemes in CRNs. The purposes are toestablish a foundation and to spark new interests in thisresearch area covering new kinds of CR networks such asCR sensor networks [4]. Our contributions are as follows.Sections 2, 3, and 4 present the functionalities, character-istics, and challenges, respectively. Section 5 presents vari-ous spectrum leasing schemes in CRNs. Section 6 presentsperformance enhancement achieved by spectrum leasingschemes. Section 7 presents open issues. Finally, we presentconclusions.

2. Functionalities of Spectrum Leasing inCognitive Radio Networks

This section discusses the functionalities of PUs and SUsfor spectrum leasing in CRNs. Generally speaking, spectrumleasing is comprised of the following functionalities.

(F1) PU’s Function

(F1.1) Determination of the Cost of White Spaces. PUsdetermine the cost (e.g., monetary price) ofwhite spaces to be imposed on SUs.

(F1.2) Determination of PUs’ and SUs’ Channel AccessTime. PUs are the rightful owners of the licensedspectrum, and so the PU Base Station (BS)may determine suitable channel access time fortransmission opportunities for both PUs and

International Journal of Distributed Sensor Networks 3

SUs. For instance, in centralized networks, thePUhosts send their respective information (e.g.,idle time) to PU BS. Subsequently, the PU BSallocates transmission opportunities for PU andSUnetworks. In other words, the PUs determinethe amount of white spaces to be leased toSUs. The objective is to maximize the networkperformance (e.g., throughput) of PUs and SUs[10, 11].

(F1.3) Relay Selection. PUs select the SUs that providethe highest gain (e.g., PU-SU linkswith the best-known signal-to-noise ratio (SNR)) as relays inorder to maximize throughput performance.

(F1.4) PUs’ Packet Transmission. PUs transmit theirown packets to destination in order to enhancetheir network performance.

(F2) SU’s Function

(F2.1) Collaborator Selection. SUs select the suitablePUs to collaborate with. This covers the eval-uation of the gain (e.g., the amount of whitespaces with sufficient SNR) and cost (resourcesrequired to relay PUs’ traffics, such as energyconsumption).

(F2.2) Determination of SU’s Channel Access Time. SUsdetermine the amounts of white spaces, whichincrease with channel access time, to requestfrom PUs based on the cost imposed by thePUs. For instance, in a Time-Division MultipleAccess (TDMA) system, SUs must determinethe optimal time duration in which they mustinvolve as relay to transmit PU packets and totransmit their own packets [8].

(F2.3) SUs’ Packet Transmission. SUs transmit pack-ets, and this involves two phases. Firstly, theSUs relay PU packets. To ensure continuouscollaboration with PUs, the SUs must achieve acertain level of network performance enhance-ment while relaying the PUs’ packets. Secondly,the SUs transmit their own packets. Spatialreuse is possible, and so the SUs must mini-mize interference among themselves [12]. Forinstance, in centralized networks, SU BS andhostsmay serve as relays to transmit PUpackets,and subsequently the SU BS allocates the whitespaces offered by PUs to its SU hosts fairly [10,13].

Spectrum leasing involves several steps and messagehandshaking, and we describe a general procedure inFigure 2. Consider two centralized PU and SU networks,which are collocated in the same area. Several PUhosts (or SUhosts) are associated with a PU BS (or SU BS).The procedureis as follows.

Step 1. ThePUhosts send information on their respective idleperiods (or white spaces) to PU BS.

J PU hosts PU BS SU BS K SU hostsStep 1

Step 2

Step 5

Step 3

Step 4

Step 6Step 7

Step 8Step 9

Step 10 Step 10

Figure 2: A general spectrum leasing procedure.

Step 2. The PU BS determines the cost (F1.1) and duration(F1.2) of white spaces. There are 𝐽 PU hosts to be leased toSUs.

Step 3. The PU BS sends the cooperation information (e.g.,the cost and duration, as well as SNR of the white spaces) toSU BS.

Step 4. The SU BS broadcasts the cooperation information toits SU hosts.

Step 5. The SU hosts determine the optimum transmissionand relaying strategies (i.e., (F2.2) and (F2.3)) using thecooperation information. If auction mechanism is applied,the SU hosts may determine bid values.

Step 6. The SU hosts send their respective decisions (e.g.,strategies and bid values) to SU BS.

Step 7. The SUBS decides to accept the lease or not and selectthe suitable PUs to collaborate with (F2.1).

Step 8. The SU BS sends its decisions to PU BS.

Step 9. The PU BS decides to lease or not and select thesuitable SUs as relays (F1.3).

Step 10. Finally, based on the lease, the PU BS transmits itspackets (F1.4) directly through a single hop, or indirectlythrough SU relay nodes, to the PU BS’s destination node.TheSU BSmay divide the white spaces and assign the access timeof each white space to each SU hosts (F2.2).The SUs transmitpackets accordingly (F2.3).

3. Characteristics of Spectrum Leasing inCognitive Radio Networks

This section discusses the characteristics of spectrum leasingin CRNs. There are three characteristics as follows.

4 International Journal of Distributed Sensor Networks

(C1) Network Topology: Centralized (C1.1) and Distributed(C1.2). In centralized networks (C1.1), a central entitywhich is usually referred as Base Station (BS) isresponsible for communications between PU andSU networks [14], whereas, in distributed networks(C1.2), BS does not exist, and PUs and SUs sharetheir information through a common control channel[14]. For instance, in [5], a centralized network (C1.1)topology is used, in which PUs are leaders andresponsible to select the most appropriate SU forcooperative communication and hence the SUs arefollowers.

(C2) Intracooperative Mode: Intracooperative (C2.1) andNonintracooperative (C2.2). The PUs may cooper-ate among themselves through an intra-cooperativeapproach in order to achieve the advantages (A1.1)-(A1.2) and (A2.1). Likewise, the SUs may adoptthe same approach. In Figure 3, the intracooperative(C2.1) mode is shown in (a) and (c) and fromthe SU’s perspective, the SUs may cooperate amongthemselves and jointly improve network-wide perfor-mance such as throughput performance, as well asto reduce the monetary and nonmonetary spectrumleasing costs imposed by PUs. In other words, a groupof SUs may lease a channel and subsequently sharethe channel among themselves in order to reducespectrum leasing costs. In Figure 3, the nonintraco-operative (C2.2) mode is shown in (b) and (d) andfrom the PU’s perspective, each PUmay competewitheach other to lease their respective white spaces andhence each PU may set a competitive price based onthe demand of channel access from SUs. From theSU’s perspective, the SUs may also compete with eachother to acquire the white spaces through auction-based mechanisms [15]. For instance, in [5], each SUoptimizes its power allocation in the transmission ofPU packets in order to fulfill the packet transmissionrequirements of PUs. This helps each SU to remaincompetitive in order to obtain white spaces in theupcoming auctions and this has been shown toimprove SU throughput performance.

(C3) Intercooperative Mode: Intercooperative (C3.1) andNonintercooperative (C3.2). PUs and SUsmay cooper-ate with each other in order to achieve the advantages(A1.1)-(A1.2) and (A2.1). In Figure 3, the intercoop-erative (C3.1) mode is shown in (c) and (d) andthe PUs and SUs cooperate with each other, and sothis improves the overall network-wide performancesuch as throughput performance. In Figure 3, thenonintercooperative (C3.2) mode is shown in (a) and(b) and the PUs and SUs are referred to as selfishusers, and they do not cooperate with each other. Forinstance, in [16], the PUs attempt to maximize theirprofit or reward out of the white spaces, while the SUsattempt to reduce their cost.

4. Challenges of Spectrum Leasing inCognitive Radio Networks

This section discusses the challenges associated with spec-trum leasing in CRNs. There are three challenges as follows.

(H1) Increasing the Monetary Gain of PUs. PUs aim toincrease their monetary gain through spectrum leas-ing. This encourages the PUs to participate in spec-trum leasing by increasing the amount of white spacesavailable to SUs. Subsequently, this increases PUs’and SUs’ throughput performance [10]. The PUs maycooperate or compete with each other to lease theirwhite spaces. As an example, in [10], PUs cooperatewith each other, and linear programming is appliedto set the optimal price of the white spaces in order toincrease their monetary gain. As another example, in[16], PUs compete with each other, and game theoryis applied to set the optimal price of the white spacesin order to increase their monetary gain.

(H2) Selecting an Optimal Channel with White Spaces bySUs. SUs aim to access the licensed channel or whitespaces in order to increase their network performance(e.g., throughput). So, this encourages the SUs toparticipate in spectrum leasing and subsequentlyincreases PUs’ and SUs’ network performance [17].However, the access to white spaces by SUs requiresmonetary cost, and so there is a need to find anoptimal channel that provides the best possible net-work performance while incurring the least possiblecost. For instance, Cao et al. [5] propose a spectrumsharing policy in which white spaces are being leasedto SUs, in order to increase the network capacity of SUnetwork.

( H3) Scheduling theChannel Access of PUs and SUs.ThePUsschedule the time for the transmissions of PUs’ andSUs’ packets in order to enhance their respective QoSperformance (e.g., throughput). The time allocationfor SUs’ links must be sufficiently higher comparedto that of PUs’ links in order to reap the benefitsof spectrum leasing [9]. Otherwise, the queue sizeat SU relay nodes may grow and eventually becomeinsufficient to accommodate new packets from bothPUs and SUs leading to packet loss. However, thewhite spaces being leased to SUsmay not be sufficientto cater for PUs’ and SUs’ packets. For instance,Huanget al. [18] propose a coalition game to allocate asuitable fraction of channel access time among PUsand SUs, so that SUs transmit PUs’ packets as well astheir own packets.

(H4) Continuous Monitoring of White Spaces Being Leasedto SUs by PUs. Upon negotiation, the PUs and SUsmay need to monitor the white spaces (e.g., amountand channel quality) and the Quality of Service (QoS)of packet transmission in order to make sure that

International Journal of Distributed Sensor Networks 5

SU plane

PU plane

(b) Non-intracooper-

ative and non-

intercooper-ative

(a) Intra-cooperative

and non-intercooper-

ative

(c) Intra-cooperativeand inter-

cooperative

(d) Non-intracooper-

ative and intercooperative

Primary user (PU)Secondary user (SU)

Figure 3: Mode of cooperation between PU and SU network.

each party follows suit. However, the continuousmonitoring of SUs requires more intelligence to beincorporated into the PU network. For instance, in[15], PUs additionally acts as an online auctioneer tomonitor the SUs activities. Likewise, in [19], PUs needto ensure that the interference caused by SUs is lessthan the acceptable interference level. Furthermore,SUs also need tomonitor the SUs’ signal level in orderto reduce interference with PUs [20].

5. Spectrum Leasing Schemes inCognitive Radio Networks

This section presents existing work on spectrum leasingschemes in CRNs. The schemes are categorized with respectto the challenges (see Section 4) and on the basis of adoptedapproaches (e.g., game theoretic approaches and nongametheoretic approaches) to address the challenges. The gametheoretic approaches, such as Stackelberg game [21], are usedto achieve the equilibrium state (e.g., Nash equilibrium [22])and it involves PUs and SUs as players of the game. Examplesof the nongame theoretic approaches are reinforcementlearning [23] and convex optimization [24]. Table 1 presentsthe gains, functions, and characteristics of the spectrumleasing schemes. The performance enhancement achieved byeach scheme is shown in Table 2 (see Section 6).

5.1. Increasing the Monetary Gain of PUs. There are six spec-trum leasing schemes that focus on addressing the challengeof increasing the monetary gain of PUs that motivates thePUs to participate in spectrum leasing. These schemes havebeen shown to increase the monetary gain of PUs, as well asto enhance PUs’ or SUs’ QoS performance (e.g., throughput).

5.1.1. SchemesThat Use GameTheoretic Approaches. Alptekinand Bener [16] propose one PU F(1) and one SU F(2)functionalities, namely, determination of the cost of whitespaces (F1.1), as well as collaborator selection (F2.1) inorder to increase PUs’ monetary gain (A1.1) and to providededicated channel access to SUs (A2.1) in centralized (C1.1)SU networks. The purpose is to maximize the PUs’ profitas seller in terms of its utility function 𝑈𝑝, which helps tosatisfy theQoS parameters (e.g., jitter) of SUs as buyers, in thepresence of 𝐽 PUs and𝐾 SUs.The functionalities aremodeledand solved using game theory and the Nash equilibrium in anonintracooperative (C2.2) mode and non-intercooperative(C3.2) mode, respectively. The cumulative utility function of𝐽 PUs is defined as

𝑈𝑝 =

𝐽

∑

𝑗=1

𝑝𝑗𝑘 ⋅ 𝑑𝑗𝑘 − 𝑐𝑗𝑘 ⋅ 𝑑𝑗𝑘, (1)

where 𝑗 = {1, 2, . . . , 𝐽}, 𝑝𝑗𝑘 is the price that PU 𝑗 imposes onSU 𝑘, 𝑑𝑗𝑘 is the demand factor (i.e., SU 𝑘’s expectation onQoSrequirement including jitter and throughput from PU 𝑗), and𝑐𝑗𝑘 is the cost associatedwith the channel leased to SU 𝑘whichmust be paid by PU 𝑗 to regulatory authorities (e.g., FederalCommunications Commission (FCC)).The PU 𝑗 determinesthe cost of white spaces (F1.1) and on that basis selects SU 𝑘if the difference between price 𝑝𝑗𝑘 and cost 𝑐𝑗𝑘 in PU utilityfunction is positive, which indicates a monetary gain for PU𝑗. The SU 𝑘 selects a PU collaborator (F2.1) to achieve its QoSlevel as indicated in the demand factor 𝑑𝑗𝑘 while paying thePU 𝑗 at the specified price 𝑝𝑗𝑘. It has been shown that PUs aremore likely to fulfill the SUs’ QoS demandwith the incrementof price 𝑝𝑗𝑘 (i.e., monetary gain).

Lin and Fang [25] propose one PU F(1) and one SU F(2)functionalities, namely, determination of the cost of whitespaces (F1.1), as well as SUs’ packet transmission (F2.3) inorder to increase PUs’ monetary gain (A1.1) and to provide

6 International Journal of Distributed Sensor Networks

Table1:Gains,fun

ctions,and

characteris

ticso

fthe

spectrum

leasingschemes.

Challeng

esRe

ferences

(A)G

ains

(F)F

unctions

(C)C

haracteristics

(A1)PU

sGains

(A2)

SUs

Gains

(F1)PU

sFun

ctions

(F2)

SUsF

unctions

(C1)Networking

topo

logy

(C2)

Intra-op

erativem

ode

(C3)

Inter-op

erativem

ode

(A1.1)

Mon

e-tary

gain

(A1.2

)Network

perfo

rmance

enhancem

ent

(A2.1)

Dedi-

cated

chan-

nel

access

(F1.1)

Deter-

mina-

tionof

the

costof

white

spaces

(F1.2

)Deter-

mina-

tionof

PUs’

and

SUs’

chan-

nel

access

time

(F1.3

)Re

lay

selection

(F1.4

)PUs’

Packet

transm

ission

(F2.1)Col-

labo

rator

selection

(F2.2)

Deter-

mina-

tionof

SU’s

chan-

nel

access

time

(F2.3)

SUs’

Packet

transm

ission

(C1.1)

Centralized

(C1.2

)Distrib

uted

(C2.1)Intra-

coop

erative

(C2.2)

Non

-intracoo

perativ

e(C

3.1)Inter-

coop

erative

(C3.2)

Non

-intercoo

perativ

e

(H1)

Increasin

gthe

mon

etary

gain

ofPU

s

(Alptekinand

Bener,2009)

[16]

××

××

××

×

(Lin

andFang

,2008)[25]

××

××

××

×

(Yietal.,2010)

[10]

××

××

××

××

××

(Kim

andSh

in,

2009)[26]×

××

××

(Son

gandLin,

2009)[13]

××

××

××

×

(Wuetal.,

2008)[7]

××

××

××

(H2)

Selectingan

optim

alchannel

with

white

spaces

bySU

s

(Chanetal.,

2011)[17]

××

××

××

××

(Vazqu

ez-Vilar

etal.,2010)[20]

××

××

××

××

(Cao

etal.,

2012)[5]

××

××

××

×

(Jayaweera

etal.,2011)

(centralized

approach)[8]

××

××

××

××

(Jayaweera

etal.,2011)

(distrib

uted

approach)[8]

××

××

××

××

(Muraw

skiand

Ekici,2011)[27]

××

××

××

××

(Torou

jeni

etal.,2011)[28]

××

××

××

××

International Journal of Distributed Sensor Networks 7

Table1:Con

tinued.

Challeng

esRe

ferences

(A)G

ains

(F)F

unctions

(C)C

haracteristics

(A1)PU

sGains

(A2)

SUs

Gains

(F1)PU

sFun

ctions

(F2)

SUsF

unctions

(C1)Networking

topo

logy

(C2)

Intra-op

erativem

ode

(C3)

Inter-op

erativem

ode

(A1.1)

Mon

e-tary

gain

(A1.2

)Network

perfo

rmance

enhancem

ent

(A2.1)

Dedi-

cated

chan-

nel

access

(F1.1)

Deter-

mina-

tionof

the

costof

white

spaces

(F1.2

)Deter-

mina-

tionof

PUs’

and

SUs’

chan-

nel

access

time

(F1.3

)Re

lay

selection

(F1.4

)PUs’

Packet

transm

ission

(F2.1)Col-

labo

rator

selection

(F2.2)

Deter-

mina-

tionof

SU’s

chan-

nel

access

time

(F2.3)

SUs’

Packet

transm

ission

(C1.1)

Centralized

(C1.2

)Distrib

uted

(C2.1)Intra-

coop

erative

(C2.2)

Non

-intracoo

perativ

e(C

3.1)Inter-

coop

erative

(C3.2)

Non

-intercoo

perativ

e

(H3)

Schedu

ling

thec

hann

elaccessof

PUsa

ndSU

s

(Chenetal.,

2011)[29]

××

××

××

××

(Huang

etal.,

2011)[18]

××

××

××

××

×

(Wangetal.,

2010)[30]

××

××

××

××

××

(Stano

jevetal.,

2008)[31]

××

××

××

××

(Wangetal.,

2010)[32]

××

××

××

××

(Zhang

etal.,

2010)[33]

××

××

××

××

(Zhu

etal.,

2012)[34]

××

××

××

(Asadu

zzam

anetal.,2011)[35]

××

××

××

××

×

(Khalil

etal.,

2011)[36]

××

××

××

××

×

(Zho

uetal.,

2011)[11]

××

××

××

××

(H4)

Con

tinuo

usmon

itorin

gof

white

spaces

being

leased

toSU

sbyPU

s

(Jayaweera

etal.,2010)[6]

××

××

××

(Jayaweera

and

Li,200

9)[19

]×

××

××

×

(Hakim

etal.,

2010)[37]

××

××

××

(Sod

agarietal.,

2011)[15]

××

××

××

×

8 International Journal of Distributed Sensor Networks

Table2:Perfo

rmance

enhancem

entsachieved

bythes

pectrum

leasingschemes.

Challenges

References

Perfo

rmance

Enhancem

ent

(P1)Lo

wer

outage

prob

ability

(P2)

Higher

outage

capacity

(P3)

Bette

rQoS

level

(P4)

Higher

energy

efficiency

(P5)

Higher

mon

etarygain

(P6)

Balanced

tradeoffbetween

spectrum

cost

andmon

etary

gain

(P7)

Balanced

tradeoffbetween

PUs’andSU

s’channelaccess

time

(P8)

Bette

rsecuritylevel

(P9)

Lower

PUs’

interfe

rencelevel

(H1)Increasin

gthem

onetary

gain

ofPU

s

(Alptekinand

Bener,2009)[16]

××

×

(Lin

andFang

,2008)[25]

×

(Yietal.,2010)

[10]

××

(Kim

andSh

in,

2009)[26]

×

(Son

gandLin,

2009)[13]

××

(Wuetal.,2008)

[7]

××

(H2)

Selectingan

optim

alchannel

with

whitespaces

bySU

s

(Chanetal.,2011)

[17]

××

××

(Vazqu

ez-Vilare

tal.,2010)[20]

××

(Cao

etal.,2012)

[5]

×

(Jayaweera

etal.,

2011)[8]

××

×

(Muraw

skiand

Ekici,2011)[27]

×

(Torou

jeni

etal.,

2011)[28]

××

(H3)

Schedu

ling

thec

hann

elaccessof

PUsa

ndSU

s

(Chenetal.,2011)

[29]

××

(Huang

etal.,

2011)[18]

××

(Wangetal.,

2010)[30]

××

(Stano

jevetal.,

2008)[31]

××

(Wangetal.,

2010)[32]

××

×

(Zhang

etal.,

2010)[33]

××

×

(Zhu

etal.,2012)

[34]

×

(Asadu

zzam

anet

al.,2011)[35]

××

×

(Khalil

etal.,

2011)[36]

×

(Zho

uetal.,2011)

[11]

×

International Journal of Distributed Sensor Networks 9

Table2:Con

tinued.

Challenges

References

Perfo

rmance

Enhancem

ent

(P1)Lo

wer

outage

prob

ability

(P2)

Higher

outage

capacity

(P3)

Bette

rQoS

level

(P4)

Higher

energy

efficiency

(P5)

Higher

mon

etarygain

(P6)

Balanced

tradeoffbetween

spectrum

cost

andmon

etary

gain

(P7)

Balanced

tradeoffbetween

PUs’andSU

s’channelaccess

time

(P8)

Bette

rsecuritylevel

(P9)

Lower

PUs’

interfe

rencelevel

(H4)

Con

tinuo

usmon

itorin

gof

whitespaces

beingleased

toSU

sbyPU

s

(Jayaweera

etal.,

2010)[6]

××

×

(Jayaweera

and

Li,2009)

[19]

××

(Hakim

etal.,

2010)[37]

××

×

(Sod

agarietal.,

2011)[15]

××

×

10 International Journal of Distributed Sensor Networks

dedicated channel access to SUs (A2.1) in distributed (C1.2)SU networks. The purpose is to maximize the PUs’ and SUs’utility functions 𝑈𝑝 and 𝑈𝑠, respectively, while taking intoaccount the mutual benefits of PUs (or sellers) and SUs(or buyers). The functionalities are modeled in the presenceof 𝐽 PUs and 𝐾 SUs and solved using a two-level gamethat is split into PU-level game and SU-level game in anon-intracooperative (C2.2) mode and non-intercooperative(C3.2) mode, respectively. In this hierarchy of games, PUscompete with each other to lease their spectrum to SUs byadjusting their price of white spaces in order to maximizetheir respective utility functions; each SU attempts to leasea certain amount of white spaces from PU that provides theoptimal quality white spaces. The PUs’ 𝑗 ∈ 𝐽 utility functionis defined as

𝑈𝑝,𝑗 =

𝐾

∑

𝑘=1

𝐵𝑗𝑘 {𝑝𝑗𝑘 − 𝑐𝑗} , (2)

where 𝐵𝑗𝑘 is the bandwidth (or white spaces) that PU 𝑗allocates to SU 𝑘, 𝑝𝑗𝑘 is the price that PU 𝑗 imposes on SU𝑘, and 𝑐𝑗 is the cost associated with the channel leased to SU𝑘 which must be paid by PU 𝑗 to regulatory authorities (e.g.,FCC). A PU decides to play a game if price 𝑝𝑗𝑘 is greater thancost 𝑐𝑗 of the leased channel (F1.1).The SUs’ utility function isdefined as

𝑈𝑠 = {log2(1 + 𝑅𝑠,𝑘) Case-I

log2(1 + 𝑅

MAX𝑠,𝑘

) Case-II,(3)

Where 𝑅𝑠,𝑘 and 𝑅MAX𝑠,𝑘

are the transmission rate, as well as itsmaximum value, of SU 𝑘. In Case-I, PU allocates lesser whitespaces to SU 𝑘 than it demands, while in Case-II, PU allocateshigher bandwidth to SU 𝑘 than it demands. The higher theamount of white spaces provided by PU to SU, the higher isthe transmission rate of SU 𝑘 (F2.3). It has been revealed thatthe number of SUs increases with the price of white spacesthat PUs impose to SU.

Yi et al. [10] propose three PU F(1) and two SU F(2)functionalities, namely, determination of the cost of whitespaces (F1.1), relay selection (F1.3) and PUs’ packet trans-mission (F1.4), as well as determination of SU’s channelaccess time (F2.2), and SUs’ packet transmission (F2.3) inorder to increase PUs’ monetary gain (A1.1) and to providededicated channel access to SUs (A2.1) in centralized (C1.1)SU networks. The purpose is to maximize the PUs’ and SUs’network utility functions, 𝑈𝑝 and 𝑈𝑠, respectively. The PUsand SUs are rational and selfish in nature. The functionalitiesare modeled and solved using Stackelberg game, in whichthe PU is the leader and the SU is the follower in an intra-cooperative (C2.1) mode and inter-cooperative (C3.1) mode,respectively. The Nash equilibriummaximizes both PUs’ andSUs’ utility functions, 𝑈𝑝 and 𝑈𝑠. The PUs’ utility function isdefined as

𝑈𝑝 = 𝑢𝑑 + 𝑢𝑟, (4)

where 𝑢𝑑 and 𝑢𝑟 are revenues. Revenue 𝑢𝑑 is dependent onthe ratio of total PUs’ packet transmissions, which include

successful packet transmissions through direct transmissions(i.e., fromPUhost to PUBS) and relaying through SUs to totaltraffic demand of all PU hosts. Revenue 𝑢𝑟 is derived from thewhite spaces being leased to SUs. The SUs’ utility function isdefined as

𝑈𝑠 = 𝑢𝑠 − 𝑢𝑟, (5)

where 𝑢𝑠 is derived from the total SUs’ packet transmissionsfrom all SU hosts. Both𝑈𝑝 and𝑈𝑠 take into account the SNRof the channels. There are two main steps in the Stackelberggame. Firstly, the PU BS (or leader) determines its strategycomprised of a set of potential SU relaying nodes (F1.3) andthe costs (i.e,. the price of white spaces per unit access time)to be imposed on SUs (F1.1) and sends the PUs’ strategy to SUBS. Using the fixed leader’s strategy, the SU BS (or follower)determines the amount of white spaces to request from PUsbased on the costs (F2.1); hence, higher cost may reduce theamount of white spaces to request. The SU BS sends the SUstrategy to PU BS. Secondly, using a fixed follower’s strategy,the PU BS selects relay nodes and finalizes the costs and startpacket transmissions (F1.4). Similarly, the SU BS allocates theleased white spaces amongst SUs for their respective packettransmission (F2.3). The spectrum leasing scheme has beenshown to increase PUs’ and SUs’ utility functions,𝑈𝑝 and 𝑈𝑠,as well as to increase the amount of white spaces being leased.This scheme also decreases the price of white spaces per unitaccess time.

5.1.2. Schemes That Use Nongame Theoretic Approaches. Kimand Shin [26] propose one PU F(1) function, namely, deter-mination of the cost of white spaces (F1.1) in order to increasePUs’ monetary gain (A1.1) in distributed (C1.2) SU networks.The purpose is to maximize the PUs’ profit by controllingthe SUs’ admission and eviction strategies. The admissionstrategy allows the SUs to utilize PUs’ channels on the basis ofthe requested amount of white spaces, which basically yieldsthe PUs’ profit. Hence, if SUs demands a small amount ofwhite spaces, then PUs may reject their admissions due tothe less monetary gain. This is because the PUs are interestedto allocate white spaces to SUs that request larger amountof white spaces in order to maximize their monetary gain,whereas the eviction strategy is set so that SUs evacuate thechannel immediately if PUs’ activities reappear. The functionis modeled and solved using semi-Markov decision processand linear programming in a non-intracooperative (C2.2)mode and non-intercooperative (C3.2) mode, respectively.The PUs allocates their underutilized channels to a group of𝑘 SUs. The expected revenue of PUs is defined as

𝑟𝑝 = ∑

𝑘∈𝐾

𝑝𝑘𝑄𝑘𝐾, (6)

where 𝑝𝑘 is the price that 𝑘 SUs pay to PU in return of itsQoS demand 𝑄𝑘, while 𝐾 is the number of SUs in the group.Higher PUs’ revenue, which comes with higher price of whitespaces (F1.1), indicates higher QoS demand from SUs. It hasbeen shown that PUs’ revenue increases with the amount ofwhite spaces. However, the PUs’ revenue decreases when thewhite spaces become oversupplied.

International Journal of Distributed Sensor Networks 11

Song and Lin [13] propose one PU F(1) and one SUfunctionalities, namely, determination of the cost of whitespaces (F1.1), as well as SUs’ packet transmission (F2.3) inorder to increase PUs’ monetary gain (A1.1) and to providededicated channel access to SUs (A2.1) in distributed (C1.2)SU networks. The purpose is to maximize the profit ofPUs while allocating the white spaces to SUs. The functionis modeled and solved using auction-based property-rightsmodel mechanism in a nonintracooperative (C2.2) modeand nonintercooperative (C3.2) mode, respectively. In aproperty-rights model, SUs are divided into non-overlappinggroups and a leader is elected from each group. The auctionmechanism is divided into time windows, and each windowis further divided into two phases, namely, auction andcommunication. There are four main purposes in regard tothe auctionmechanism. Firstly, it maximizes the overall spec-trum utilization. Secondly, it maximizes the number of SUwinners (or SU groups that gain a channel). Thirdly, it fulfillsthe bandwidth requirement of SUs. Note that the channelsare heterogeneous and each channel has different amount ofbandwidth (or white spaces). Fourthly, it maximizes the PUs’revenue. In a round of bidding, each SU leader determines abid value based on hungry degree, which takes into accountthe amount of white spaces required by its group of SUs.During the auction phase, the PU auctions off 𝑛 channelswith white spaces to 𝑚 SU leaders in two phases. Each SUleader uses an auction phase, which is based on its bandwidthrequirement, to bid for a leasing channel. Higher value ofhungry degree leads to higher bid value. During the firstphase of auction, in order to meet the first, second, and thirdpurposes, the PU grants channels to as many groups of SUsas possible to meet their respective minimum requirementon the amount of white spaces. During the second phaseof auction, in order to achieve the fourth purpose, the PUallocates the channels with white spaces to SU leaders thatoffer higher bid values (F1.1). During the communicationphase (F2.3), the SUs transmit packets and the PU keeps trackof availablewhite spaces for auctions in the next timewindow.The spectrum leasing scheme has been shown to increasethroughput performance in regard to vacant channels.

Wu et al. [7] propose one PU F(1) function, namely,determination of the cost of white spaces (F1.1) in order toincrease PUs’ monetary gain (A1.1) and to provide dedicatedchannel access to SUs (A2.1) in centralized (C1.1) SU net-works. The purpose is to maximize the PU monetary gainand SUs network utility function 𝑈𝑠, while preventing thecollusive SUs to access the PUs’ white spaces. The collusiveSUs form a coalition and deliberately decrease the priceof white spaces offered by PUs. The function is modeledand solved using binary linear programming and convexoptimization in an intra-cooperative (C2.1) mode and non-intercooperative (C3.2) mode, respectively. Binary linearprogramming is a mathematical method to determine theoptimal results that comprises binary integers (i.e., 0 and1). The PU sells white spaces to 𝐾 SUs with the assistancefrom a third-party spectrum broker. Upon the reception ofbid values 𝑏𝑘 = {𝑏1, 𝑏2, . . . , 𝑏𝐾} from 𝐾 SU, the spectrumbroker announces the winning SUs by defining the channelallocation 𝑥𝑘 = {𝑥1, 𝑥2, . . . , 𝑥𝐾} and the associated price

𝑝 = {𝑝1, 𝑝2, . . . , 𝑝𝐾} for 𝐾 SUs. For the winning SUs, thechannel allocation 𝑥𝑘 is set to one (i.e., 𝑥𝑘 = 1), whichindicates that the channel has been allocated to winner SU𝑘. The gain of each winning SUs is 𝑔𝑘, which lead to anefficient channel allocation which is used to compute theutility function of SUs; that is,𝑈𝑠 = ∑

𝐾

𝑘=1𝑔𝑘 ⋅𝑥𝑘. Higher values

of 𝑈𝑠 indicate higher number of winning SUs in the auctionfor white spaces. It has been shown that, as the number ofwinning SUs increases, the price of the white spaces imposedby the PUs as sellers also increases.

5.2. Selecting an Optimal Channel with White Spaces by SUs.There are six spectrum leasing schemes that focus onmotivat-ing the SUs to participate in spectrum leasing by increasingthe amount of white spaces for SUs. These schemes havebeen shown to enhance PUs’ or SUs’ QoS performance (e.g.,throughput).

5.2.1. Schemes That Use Game Theoretic Approaches. Chanet al. [17] propose two PU F(1) and one SU F(2) func-tionalities, namely, determination of PUs’ and SUs’ channelaccess time (F1.2) and relay selection (F1.3), as well as SUs’packet transmission (F2.3) in order to enhance the networkperformance of PUs (A1.2) and to provide dedicated channelaccess to SUs (A2.1) in centralized (C1.1) SU networks. Thepurpose is to maximize the spectrum utilization of PUand SU networks by adopting the cooperation strategiesin between of 𝐽 PUs and 𝐾 SUs in the form of PUs andSUs utility functions, 𝑈𝑝 and 𝑈𝑠, respectively. In separatecooperation, PU 𝑗 and SU 𝑘 form a one-to-one collaborativerelationship with each other, while in grand cooperation, PUsand SUs form a coalition that comprises of many one-to-oneand one-to-many collaborative relationships with each other.The functionalities are modeled and solved using canonicalcoalition game theoretic framework and convex optimizationproblem in a non-intracooperative (C2.2) mode and inter-cooperative (C3.1)mode, respectively.ThePUutility functionis defined as

𝑈𝑝 = 𝑢 (𝑅𝑝) + 𝑝𝑠𝑝𝑗− 𝐿 (𝑐𝑝) , (7)

where 𝑢(⋅) and 𝐿(⋅) are concave function that maps the PUachievable transmission rate 𝑅𝑝 as utility gain and PU cost 𝑐𝑝as utility loss, while 𝑝𝑠𝑝𝑗 is the price of white spaces that PU 𝑗imposes on SUs. The SU utility function is defined as

𝑈𝑠 = 𝑟 (𝑅𝑠) + 𝑃𝑠𝑘𝑝, (8)

where 𝑟(⋅) is (⋅) concave function that projects SU achievablerate 𝑅𝑠 as revenue and 𝑃𝑠𝑘𝑝 is the price that PUs imposes onSU 𝑘 in order to lease its channel. It has been shown that thegrand cooperation strategy produces higher optimal utilityvalue than individuals’ cooperation.

Vazquez-Vilar et al. [20] propose two PU F(1) and oneSU F(2) functionalities, namely, relay selection (F1.3), andPUs’ packet transmission (F1.4), as well as SUs’ packet trans-mission (F2.3) in order to enhance the network performanceof PUs (A1.2) and to provide dedicated channel access toSUs (A2.1) in centralized (C1.1) SU networks. The purpose

12 International Journal of Distributed Sensor Networks

is to maximize the PUs’ and SUs’ utility functions 𝑈𝑝 and𝑈𝑠 in the presence of a PU communication node pair inorder to minimize the SUs’ interference to PUs by reducingtheir power consumption.The PU determines the maximumallowable interference that PU can tolerate from SUs 𝐼𝑝max,while the SUs aim to reduce their transmission power in orderto fulfill the requirement 𝐼𝑝max. The function is modeled andsolved using Stackelberg game in an intracooperative (C2.1)mode and intercooperative (C3.1) mode, respectively. In thisscheme, the PU is the leader and the SU is the follower. Tofoster collaboration with SUs, the PU maximizes its utilityfunction, and it is defined as

𝑈𝑝 = 𝑢𝑝 (𝐼𝑠

𝑘, Δ𝑇𝑝

𝑝) , (9)

where 𝑢𝑝 increases with the increment of interference fromSU 𝑘 (or 𝐼𝑠

𝑘≤ 𝐼𝑝

max) and decreases with the increment of PUs’transmission powerΔ𝑇𝑝𝑝 . To foster collaborationwith PU, theSU maximizes its utility function, and it is defined as

𝑈𝑠 = 𝑢𝑠 (𝑅𝑠

𝑘, 𝐼𝑠

𝑘) , (10)

where 𝑢𝑠 increases with the increment of the SU transmissionrate𝑅𝑠

𝐾and decreases with interference fromSU 𝐼𝑠

𝑘. Note that,

𝑅𝑠

𝑘(𝑝𝑘) and 𝐼

𝑠

𝑘(𝑝𝑘) increase with the SU transmission power

𝑝𝑘. Maximizing𝑈𝑠 helps tomaximize the SU 𝑘’s power vector𝑃𝐼𝑝max𝐾

= argmax𝑝{𝑢𝑠(𝑅𝑠

𝑘, 𝐼𝑠

𝑘)}. This has led to computing the

overall utility function of PUs and SUs on the basis of 𝐼𝑝max.ThePU selects a SU relay node 𝑘 ∈ 𝐾 (F1.3) that has the lowesttransmission power for transmission of PU packets (F1.4),as well as SUs’ packets (F2.3) among the other SUs. It hasbeen shown that the proposed scheme achieves higher utilityfunction for both PUs and SUs compared to the traditionalscheme.

5.2.2. SchemesThat Uses NongameTheoretic Approaches. Caoet al. [5] propose twoPUF(1) and one SUF(2) functionalities,namely, relay selection (F1.3) and PUs’ packet transmission(F1.4), as well as SUs’ packet transmission (F2.3) in orderto enhance the network performance of PUs (A1.2) incentralized (C1.1) SU networks. The purpose is to maximizethe spectrum utilization of PU and SU networks, wherethe PU and SU BSs operate in an intracooperative (C2.1)mode and intercooperative (C3.1) mode, respectively.The PUsource node 𝑖 selects the best available SU relay node 𝑘, andestablishes communication with the PU destination node 𝑗.The SU relay is used to transmit PU and SU packets usinga quadrature modulation scheme, which depends on twofactors, namely, power allocation factor 0 ≤ 𝐹𝑝

𝑖𝑗,𝑘≤ 1 and

weight factor 0 ≤ 𝑤𝑝𝑖𝑗,𝑘

≤ 1. The power allocation factordetermines the transmission of packets through SU relaynode. Note that the SU relay node transmits PU packets onlyif 𝐹𝑝𝑖𝑗,𝑘

= 1, the SU packets only if 𝐹𝑝𝑖𝑗,𝑘

= 0, and both PUs’ andSUs’ packets if 0 < 𝐹𝑝

𝑖𝑗,𝑘< 1, whereas the weight factor deter-

mines the respective throughputs of PU and SU network,respectively. The selected SU relay node 𝑘 transmits PU andSU packets simultaneously using transmission power 𝑃𝑠

𝑖𝑗,𝑘

in two orthogonal channels (i.e., in-phase and quadraturechannels) exploited using a quadraturemodulation approach.The SU relay node relays PU packets using transmissionpower 𝐹𝑝

𝑖𝑗,𝑘⋅𝑃𝑠

𝑖𝑗,𝑘using in-phase channel and sends SU packets

using transmission power (1 − 𝐹𝑝𝑖𝑗,𝑘) ⋅ 𝑃𝑠

𝑖𝑗,𝑘in quadrature

channel. The throughput of PUs and SUs is represented bya weighted sum throughput 𝑇𝑇, which is defined as

𝑇𝑇 = (1 − 𝑤𝑝

𝑖𝑗,𝑘) ⋅ 𝑇𝑝 + 𝑤

𝑝

𝑖𝑗,𝑘⋅ 𝑇𝑠, (11)

where 𝑇𝑝 and 𝑇𝑠 represent PUs’ and SUs’ throughput, respec-tively. Note that 𝑇𝑇 = 𝑇𝑝 if 𝑤

𝑝

𝑖𝑗,𝑘= 0 and 𝑇𝑇 = 𝑇𝑠 if 𝑤

𝑝

𝑖𝑗,𝑘= 1,

while𝑇𝑝 and𝑇𝑠 achieve a balance if𝑤𝑝

𝑖𝑗,𝑘= 1/2. A primal-dual

subgradient algorithm, including Lagrange multipliers andthe Karush-Kuhn-Tucker conditions, is used to optimize 𝐹𝑝

𝑖𝑗,𝑘

and 𝑃𝑠𝑖𝑗,𝑘

in order to optimize the weighted sum throughput𝑇𝑇. The PU selects a SU only if it improves throughputperformance (F1.3), while the selected SU transmits the PUand SU packets simultaneously (F1.4), or the SU packetsonly (F2.3) when the PU is inactive. Through achievingbalanced throughputs 𝑇𝑝 and 𝑇𝑠, the scheme has been shownto maximize 𝑇𝑇, and this is due to the dependence of 𝑇𝑝 and𝑇𝑠 on power allocation factor 𝐹

𝑝

𝑖𝑗,𝑘and weight factor 𝑤𝑝

𝑖𝑗,𝑘.

Jayaweera et al. [8] propose two PU F(1) and one SU F(2)functionalities, namely, relay selection (F1.3) and PUs’ packettransmission (F1.4), as well as SUs’ packet transmission (F2.3)in order to enhance the network performance of PUs (A1.2)and to provide dedicated channel access to SUs (A2.1) incentralized (C1.1) and distributed (C1.2) SU networks. Thepurpose is to maximize the PUs’ and SUs’ utility functions𝑈𝑝and 𝑈𝑠, respectively, in terms of power savings of PUs whenthey collaborate with SUs in the presence of 𝐽 PUs and𝐾 SUs.For centralized CRNs, the functionalities are modeled andsolved using reinforcement learning in an intracooperative(C2.1) mode, and intercooperative (C3.1) mode, respectively,whereas for distributed CRNs, the functionalities are mod-eled and solved using reinforcement learning in a nonintra-cooperative (C2.2) mode and inter-cooperative (C3.1) mode,respectively. The PU 𝑗 ∈ 𝐽 utility function is defined as

𝑈𝑝 =𝑃𝑗,𝑑 − 𝑃𝑗 (𝑃𝑘(𝑗),𝑗)

𝑃𝑗,𝑑(𝑅𝑗 (𝛼𝑗) − 𝑅𝑗,min) , (12)

where 𝑃𝑗,𝑑 is the maximum transmission power of PU 𝑗through direct PU-PU transmission without using a SU relaynode, 𝑃𝑗(𝑃𝑘(𝑗),𝑗) is the PU 𝑗 transmission power through PU-SU-PU transmission using SU 𝑘 as a relay node where 𝑃𝑘(𝑗)is the transmission power for SU 𝑘 to relay the PUs’ packetsto its destination, and 𝑅𝑗(𝛼𝑗) and 𝑅𝑗,min are the achievabletransmission rate of PU 𝑗 after allocating 𝛼𝑗 of white spacesto SUs and the minimum transmission rate of PU 𝑗 for directtransmission, respectively. The SU 𝑘 ∈ 𝐾 utility function isdefined as

𝑈𝑠,𝑘 = 𝛼𝑗𝑊𝑗 log (1 + SNR𝑘,𝑖) (BER𝑘𝑗,min − BER𝑘𝑗,(𝑃𝑘(𝑗))) ,(13)

International Journal of Distributed Sensor Networks 13

where 𝑊𝑗 is the bandwidth used by SU to transmit its ownsignal, SNR𝑘,𝑖 is the signal-to-noise ratio of SU 𝑘, whileBER𝑘𝑗,min and BER𝑘𝑗,(𝑃𝑘(𝑗)) are the minimum and observedBit Error Rate (BER) values of SU 𝑘 while relaying PU j’spackets. It has been shown that the transmission power of PUdecreases with increasing the transmission power of SU.

Murawski and Ekici [27] propose two PU F(1) andone SU F(2) functionalities, namely, relay selection (F1.3)and PUs’ packet transmission (F1.4), as well as SUs’ packettransmission (F2.3) in order to enhance the network per-formance of PUs (A1.2) and to provide dedicated channelaccess to SUs (A2.1) in distributed (C1.2) SU networks. Thepurpose is to maximize the throughput of PUs and SUsin an intra-cooperative (C2.1) mode and inter-cooperative(C3.1) mode, respectively. The network considers a singlePU source node that communicates with a PU destinationnode through direct PU-PU transmission or indirect PU-SU-PU transmission via SU relay node. The PU destinationnode transmits Request to Send (RTS), while the SU replieswith Request to Cooperate (RTC) composed of channel stateinformation upon receiving RTS from the PU. Subsequently,the PUdestinationnode selects the suitable SUs as relay nodesusing the channel state information.The criterion adopted byPU for selecting a suitable SU relaying node is based on thebasis of higher throughput value of a given PU-SU-PU linkwith respect to the throughput value of PU-PU direct link.The PU destination node sends clear to coordinate (CTC)message to a selected SU relay node, which indicates that agiven PU-SU-PU link offers higher throughput than the PU-PU direct link; whereas, if the throughput being offered bythe PU-SU-PU link is lower than the PU-PU direct link, thenthe PU destination node sends clear to send (CTS) messageto the SU relay node, which indicates that the direct link ofPU-PU communication can take place. For the calculationof expected throughput value either from PU-SU-PU link orfrom PU-PU direct link, abackoff mechanism of distributedcoordination function [38] is used. The expected throughputvalue is dependent on the probability of successful packettransmission 𝑃𝑠, packet transmission time 𝑡packet, collisiondetection time 𝑡collide, and the expected size of PU packets𝐸packet size. Furthermore, for attaining a higher throughputgain, adaptive modulation schemes (e.g., BPSK, QPSK, and16-QAM) is used with respect to the SNR of the channels.It has been shown that, higher throughput can be achievedby changing the adaptive modulation scheme from BPSKto QPSK, and from QPSK to 16-QAM. Additionally, higherthroughput of PUs can be achieved by reducing the numberof SUs as relaying nodes which reduces the communicationoverheads.

Toroujeni et al. [28] propose two PUF(1) and one SUF(2)functionalities, namely, relay selection (F1.3) and PUs’ packet,transmission (F1.4), as well as SUs’ packet transmission (F2.3)in order to enhance the network performance of PUs (A1.2)and to provide dedicated channel access to SUs (A2.1) indistributed (C1.2) SUnetworks.The purpose is to increase thelink reliability by maximizing the transmission rate of a PUcommunication node pair and𝐾 SUs.The functionalities aremodeled and solved using Orthogonal Frequency Division

Multiplexing (OFDM) [39] symbols in an intra-cooperative(C2.1) mode and inter-cooperative (C3.1) mode, respectively.There are a total of 𝑁𝑠 + 𝑁𝑝𝑝 OFDM symbols, in which 𝑁𝑝𝑝symbols are dedicated for a PU-PUcommunication node pairfor direct transmission, and the 𝑁𝑠 symbols are dedicatedfor PU-SU and SU-SU transmissions, respectively. The PUselects the maximum transmission link 𝑅𝑝 either from PU-PU direct link 𝑅𝑝𝑝 or from PU-SU-PU relayed link 𝑅𝑝𝑠𝑝, andit is defined as

𝑅𝑝 = max {𝑅𝑝𝑝, 𝑅𝑝𝑠𝑝} . (14)

Each SU 𝑘 ∈ 𝐾 chooses the best channel to relay the packetsfrom PU source node to PU destination node as well as itsown packets to another SU. The SU cooperates with PU ifSU-SU transmission rate 𝑅𝑠𝑠 is equal to the price 𝑝𝑘, whichis charged by the PU, times the SU-PU transmission rate 𝑅𝑠𝑝,and it is defined as

𝑅𝑠𝑠 =

𝐾

∑

𝑘=1

𝑝𝑘 ⋅ 𝑅𝑠𝑝. (15)

Higher value of 𝑅𝑠𝑠 indicates higher achievable transmissionrate between SU relay node and PU destination node. Ithas been shown that as the distance increases between PUsource node and SUs, it decreases the number of selected SUsas relaying nodes. Furthermore, higher cost being incurredby SUs reduces the achievable transmission rates of PUsalthough it increases the achievable transmission rates of SUs.

5.3. Scheduling the Channel Access of PUs and SUs. Thereare ten spectrum leasing schemes that focus on schedulingof channel access time in between of PUs and SUs for theirrespective transmission. These schemes have been shown toenhance PUs’ and SUs’ QoS performance (e.g., throughput).

5.3.1. Schemes That Use Game Theoretic Approaches. Chenet al. [29] propose two PU F(1) and one SU F(2) func-tionalities, namely, determination of PUs’ and SUs’ channelaccess time (F1.2) and relay selection (F1.3), as well as SUs’packet transmission (F2.3) in order to enhance the networkperformance of PUs (A1.2) and to provide dedicated channelaccess to SUs (A2.1) in distributed (C1.2) SU networks.The purpose is to maximize the PUs’ and SUs’ networkutility functions 𝑈𝑝 and 𝑈𝑠 in the presence of 𝐽 PUs and𝐾 SUs. The functionalities are modeled and solved using athree-tier game in a non-intracooperative (C2.2) mode andnonintercooperative (C3.2) mode, respectively. The PU andSU network communicate with each other using a controlchannel protocol in order to participate and achieve a gameequilibrium. Both PUs and SUs are rational in nature. ThePU selects the suitable SUs as relay nodes to transmit PU’spackets in order to increase its transmission rate and the SUsin return achieve a portion of channel access time set bythe PU to maximize their transmission rate. The PU dividesthe transmission period into three phases. The first phase isfor primary transmission (PU-PU and PU-SU) during whichthe PUs transmit their packets to other PUs and SUs. Thesecond phase is for relayed transmission (SU-PU) during

14 International Journal of Distributed Sensor Networks

which the SUs help the PUs to relay PUs’ packets, whereasthe third phase is for secondary transmission (SU-SU) duringwhich the SUs transmit their own packets. The length of theprimary transmission phase is𝛼, the relay nodes transmissionphase is (1−𝛼)(1−𝛽), and the secondary transmission phaseis (1 − 𝛼)𝛽. Higher value of 𝛼 indicates that PUs is willing tolease its spectrum to SUs while higher value of 𝛽 encouragesSUs to collaborate and relay PUs’ packets. Thus, the PU mustdetermine optimal values of 𝛼 and 𝛽 (F1.2) that maximize itsown and SUs’ transmission rate. The PU 𝑗 utility function isdefined as

𝑈𝑝,𝑗 = min {𝛼𝑅𝑝𝑠,𝑘, (1 − 𝛼) (1 − 𝛽) 𝑅𝑠𝑝,𝑘} , (16)

where 𝑅𝑝𝑠 and 𝑅𝑠𝑝 are the maximum transmission ratethrough SU relay nodes (F1.3). The SU 𝑘 utility function isdefined as

𝑈𝑠 = 𝛽𝑅𝑠𝑠 − (1 − 𝛼) 𝑝𝑠𝑃𝑠, (17)

where 𝑝𝑠 is the cost of per unit power 𝑃𝑠 consumed by SU𝑘 as relay node to transmit PU source node packet to PUdestination node. Therefore, the utility function of SU 𝑘 isthe difference between its revenue in terms of achievable rate𝑅𝑠𝑠 (F2.3) and the cost of power which SU 𝑘 must bear inorder to relay the PU’s packets. It has been shown that asthe distance increase between the PU and SUs, their utilityfunctions increase until a certain limit which then decrease.

Huang et al. [18] propose three PU F(1) and one SUF(2) functionalities, namely, determination of the cost ofwhite spaces (F1.1), determination of PUs’ and SUs’ channelaccess time (F1.2), and relay selection (F1.3), as well as SUs’packet transmission (F2.3) in order to enhance the networkperformance of PUs (A1.2) and to provide dedicated channelaccess to SUs (A2.1) in centralized (C1.1) SU networks. Thepurpose is to maximize the PUs’ and SUs’ utility functions𝑈𝑝and𝑈𝑠 in the presence of 𝐽PUs and𝐾 SUs.The functionalitiesare modeled and solved using canonical coalition game inan intracooperative (C2.1) mode and intercooperative (C3.1)mode, respectively. The PU divides a unit time slot intothree subslots for primary transmission (PU-PUandPU-SU),relayed transmission (SU-PU), and secondary transmission(SU-SU), respectively.The length of the primary transmissionsubslot is 1−𝛼, the relay nodes transmission subslot is 𝛽, andthe secondary transmission subslot is 𝛼 − 𝛽. Higher value of𝛼 indicates that PUs are willing to lease their spectrum toSUs while higher value of 𝛽 encourages SUs to collaboratemore and relay PU packets. Thus, the PU must determinethe optimal values of 𝛼 and 𝛽 that maximize its own as wellas SUs’ transmission rate. The PU 𝑗 ∈ 𝐽 utility function is𝑈𝑝 = 𝐹(𝑅𝑝), where 𝐹(⋅) is an increasing concave functionthat represents PUs’ gain and 𝑅𝑝 is the minimum achievabletransmission rate, which can be either from PU-SU or fromSU-PU, and dependent on transmitter power 𝑃𝑡, channelgain 𝐺, and noise level 𝜎2. The SUs’ utility function is 𝑈𝑠 =𝐺(𝑅𝑠) − 𝑝𝑠, where G(⋅) is an increasing concave function thatrepresents SUs’ gain and 𝑝𝑠 is the price that SU needs to payin order to lease channels from PUs. It has been shown thatas the SUs’ channel access time increases, the transmission

rate of SUs increases significantly, which increases the PUsmonetary gainwhile decreasing its transmission rate since SUuses more power to transmits its own packets.

Wang et al. [30] propose three PU F(1) and two SUF(2) functionalities, namely, determination of the cost ofwhite spaces (F1.1), determination of PUs’ and SUs’ channelaccess time (F1.2), and relay selection (F1.3), as well asdetermination of SU’s channel access time (F2.2) and SUs’packet transmission (F2.3) in order to enhance the networkperformance of PUs (A1.2) and to provide dedicated channelaccess to SUs (A2.1) in centralized (C1.1) SU networks. Thepurpose is to maximize the PUs’ and SUs’ utility functions𝑈𝑝and 𝑈𝑠, respectively, in the presence of a PU communicationnode pair and 𝐾 SUs. The functionalities are modeled andsolved using Stackelberg game in an intra-cooperative (C2.1)mode and inter-cooperative (C3.1) mode, respectively. ThePU divides the transmission period into three phases. Thefirst phase is for primary transmission (PU-PU and PU-SU) during which the PUs transmit their packets to otherPUs and SUs. The second phase is for relayed transmission(SU-PU) during which the SUs help the PUs to relay PUs’packets whereas the third phase is for secondary transmission(SU-SU) during which the SUs transmit their own packets.The length of the primary transmission phase is (𝑇 − 𝑡𝑠)/2,the relay nodes transmission phase is (𝑇 − 𝑡𝑠)/2, and thesecondary transmission phase is 𝑡𝑠. The PU utility functionis defined as

𝑈𝑝 = 𝐺SNR (SNR𝑝𝑝 + SNR𝑝𝑠𝑝)𝑇 − 𝑡𝑠

2𝑇, (18)

where 𝐺SNR is the channel gain per unit SNR and SNR𝑝𝑝 andSNR𝑝𝑠𝑝 are the SNR values of PU-PU direct link and PU-SU-PU relayed link whereas, the SUs’ utility function is definedas

𝑈𝑠 = 𝐺𝑡𝑠− 𝑐

𝑇 − 𝑡𝑠

2{(SNR𝑝𝑠 + 1) 𝑝⋅𝑡𝑠 ⋅ 𝜎

2

(SNR𝑝𝑠 − 𝑝𝑡𝑠)𝐺𝑝𝑝} , (19)

where 𝑐 is the cost per unit energy consumption, 𝑝 is theprice that SUs needs to bear in order to buy white spaces fromPUs, and 𝜎2 is the noise variance. It has been shown that asthe distance increase between the PU and SUs, their utilityfunctions increase until a certain limit which then decrease.

Stanojev et al. [31] propose two PU F(1) and one SUF(2) functionalities, namely, determination of PUs’ and SUs’channel access time (F1.2) and relay selection (F1.3), aswell as SUs’ packet transmission (F2.3) in order to enhancethe network performance of PUs (A1.2) and to providededicated channel access to SUs (A2.1) in distributed (C1.2)SU networks. The purpose is to maximize the PUs’ trans-mission rate and the SUs’ utility function. The PU divides aunit time slot into three subslots for primary transmission(PU-PU and PU-SU), relayed transmission (SU-PU), andsecondary transmission (SU-SU), respectively. The length ofthe primary transmission subslot is (1 − 𝛼) ⋅ 𝑡slot, the relaynodes transmission subslot is 𝛼 ⋅ 𝛽 ⋅ 𝑡slot, and the secondarytransmission subslot is 𝛼 ⋅ (1 − 𝛽) ⋅ 𝑡slot. Higher value of 𝛼 andlower value of 𝛽 encourage SUs to collaborate, and so the PU

International Journal of Distributed Sensor Networks 15

must determine optimal values of 𝛼 and 𝛽, while maximizingits own transmission rate. The functionalities are modeledand solved using Stackelberg game in a nonintracooperative(C2.2) mode and intercooperative (C3.1) mode, respectively.In this scheme, PU is the leader and SU is the follower. Thegame aims to foster collaboration between PUs and SUs bymaximizing the PUs’ transmission rate and enhancing theSUs’ utility function. The PU source node 𝑖 chooses a setof SU relay node 𝑘 that provides an optimum value of PUtransmission rate, which is dependent on the transmissionrate from PU source node 𝑖 to SU relaying node 𝑘, or 𝑅𝑝𝑠

𝑖𝑗,𝑘,

while SU relaying node 𝑘 calculates the transmission ratefrom SU relay node 𝑘 to PU destination node 𝑗, or 𝑅𝑠𝑝

𝑖𝑗,𝑘, as

well as 𝛽. Hence, the value of 𝛽 must be chosen carefully toencourage collaboration between PU and SU. The choice of𝛽 must maximize the SU-PU transmission rate (𝛼 ⋅ 𝛽 ⋅ 𝑡slot) ⋅𝑅𝑠𝑝

𝑖𝑗,𝑘, on the other hand, the choice of 𝛼 must maximize

the SU-SU transmission rate {(𝛼 ⋅ (1 − 𝛽)) ⋅ 𝑡slot} ⋅ 𝑅𝑠𝑠

𝑘. The

optimal value of 𝛽 is 𝛽 = argmax𝛽∈[0,1]

𝛽 ⋅ 𝑅𝑠𝑝

𝑖𝑗,𝑘and 𝛽 is

applied in the calculation of �̂� = 𝑓(1/𝛽).The PU source nodeselects a suitable SU relay node (F1.3) to transfer its packetsto PU destination node (F1.4) if SU relay node provideshigher transmission rate; otherwise, it chooses PU-PU directlink. The PU calculates channel access time for PUs andSUs (F1.2). It has been shown that, as the number of SUrelay nodes increases, the outage probability of PU decreasesand the transmission rate of SUs increases. The SUs aim tomaximize their utility function in order to transmit its ownpackets (F2.3). The SUs utility function is 𝑢𝑠𝑠

𝑃𝑘,𝑃−𝑘, where 𝑃𝑘

is the transmission power of SU relaying node 𝑘, and 𝑃−𝑘 isa vector of the transmission power of the SU nonrelayingnodes. The PU adjusts 𝛽 to determine the time distribu-tion among PUs’ and SUs’ (F1.2) transmissions, and this isfollowed by the selection of the best available SUs as relaynodes (F1.3) for possible communication between a PU nodepair. It has been shown that the PUs’ and SUs’ throughputperformances can be increased by increasing the number ofSU relay nodes 𝑘 and decreasing the distance between PU andSU.

Wang et al. [32] propose two PU F(1) and one SU F(2)functionalities, namely, determination of PUs’ and SUs’ chan-nel access time (F1.2) and relay selection (F1.3), as well as SUs’packet transmission (F2.3) in order to enhance the networkperformance of PUs (A1.2) and to provide dedicated channelaccess to SUs (A2.1) in distributed (C1.2) SU networks. Thepurpose is to maximize the PUs’ and SUs’ utility functions𝑈𝑝and𝑈𝑠 in the presence of a PU communication node pair and𝐾 SUs. The functionalities are modeled and solved using thegame theoretic approach and the Stackelberg equilibrium ina nonintracooperative (C2.2) mode and nonintercooperative(C3.2) mode, respectively. In this game theoretic approach,PUs and SUs are rational in nature, in which the PUs andSUs attempt to achieve their respective equilibrium point.The PU selects suitable SUs that transmit PU packets as relayusing their respective transmission power, while the SUs inreturn achieve a portion of channel access time set by the PUto transmit their own packets. The PU divides a unit time

slot into two sub-slots for primary transmission (PU-PU,PU-SU, and SU-PU) and secondary transmission (SU-SU),respectively. The length of the primary transmission subslotis 𝛼, while the secondary transmission subslot is 1−𝛼. Highervalue of 𝛼 indicates that PUs are willing to lease its spectrumto SUs in order to maximize its packet transmission whileallocating the remaining time to SUs for their own packettransmission. Thus, PU must determine the optimal value of𝛼 (F1.2) thatmaximize its own and SUs’ transmission rate.ThePU utility function is defined as

𝑈𝑝 = 𝛼𝑅𝑝 (𝛼) , (20)

where 𝑅𝑝(𝛼) is the achievable transmission rate through SUrelay nodes (F1.3) and it is dependent on transmitter power𝑃𝑡, channel gain 𝐺, and noise variance 𝜎

2. The SUs’ utilityfunction is defined as

𝑈𝑠 = 𝑟𝑘 (𝑅𝑘) 𝑡𝑘 −1

2𝛼𝑃𝑘, (21)

where 𝑟𝑘, 𝑅𝑘, and 𝑡𝑘 are the revenue, achievable transmissionrate, and allocation time of SU 𝑘, and 𝑃𝑘 is the transmissionpower used by SU 𝑘 to relay the PUs’ packets to PUdestination and therefore it is considered as a cost by SU𝑘. Therefore, the utility function of SU 𝑘 is the differencebetween its revenue in terms of achievable transmission rate(F2.3) and the energy cost that SU 𝑘 must bear to relay thePUs’ packets. It has been shown that PUs’ utility functionincreases with the increment of the 𝛼 value. Furthermore,as the distance between PUs and SUs decreases, it increasestheir utility functions significantly because of higher channelgain.

Zhang et al. [33] propose two PU F(1) and one SUF(2) functionalities, namely, determination of PUs’ and SUs’channel access time (F1.2), relay selection (F1.3), and SUs’packet transmission (F2.3) in order to enhance the networkperformance of PUs (A1.2) and to provide dedicated channelaccess to SUs (A2.1) in distributed (C1.2) SU networks. Thepurpose is to maximize the PUs’ and SUs’ utility functions𝑈𝑝 and 𝑈𝑠 in order to enhance their transmission rate in thepresence of a PU communication node pair and 𝐾 SUs. Thefunctionalities are modeled and solved using game theoryand the Nash equilibrium in a non-intracooperative (C2.2)mode and inter-cooperative (C3.1) mode, respectively. Inthis game, the PU selects the suitable SUs as relay nodesto transmit PUs’ packets using their respective transmissionpower and in return, the SUs receive a portion of channelaccess time set by the PU to transmit their own packets. ThePU divides a unit time slot into three subslots for primarytransmission (PU-PU and PU-SU), relayed transmission(SU-PU), and secondary transmission (SU-SU), respectively.The length of the primary transmission subslot is 1 − 𝛼, therelay nodes transmission subslot is 𝛼𝛽, and the secondarytransmission subslot is 𝛼(1 − 𝛽). Higher value of 𝛼 indicatesthat PUs are willing to lease its white spaces to SUs whilehigher value of 𝛽 encourages SUs to collaborate more andrelay PU packets. Thus, the PU must determine optimal

16 International Journal of Distributed Sensor Networks

values of 𝛼 and 𝛽 (F1.2) that maximize its own and SUs’transmission rate. The PUs’ utility function is defined as

𝑈𝑝 = 𝑅𝑝𝑠𝑝 − 𝑅𝑝𝑝 + 𝛼𝑐𝑝𝑃𝑝, (22)

where 𝑅𝑝𝑠𝑝 and 𝑅𝑝𝑝 are the achievable transmission ratethrough SU relay nodes (F1.3) and PU-PU direct transmis-sion. These rates are dependent on transmission power 𝑃channel gain 𝐺 and noise power𝑁 whereas 𝑐𝑝 is the cost perunit of transmission power consumed by PU source node totransmit its packets to SUs and PU destination node.The SUs’utility function is defined as

𝑈𝑠 = 𝛼 (1 − 𝛽) log2 (1 +𝑃𝑠𝐺𝑠

𝑁) − 𝛼𝑐𝑠𝑃𝑠, (23)

where 𝑐𝑠 is the cost per unit transmission power consumed bySU relay node 𝑘 to transmit PU source node’s packets to PUdestination node.Therefore, the utility function of SU 𝑘 is thedifference between its revenue in terms of the achievable rate(F2.3) and the energy cost that SU 𝑘 must bear to relay thePUs’ packets. It has been shown that, as the distance increasesbetween the PU and SUs, their utility function increases untila certain limit which then decreases.

Zhu et al. [34] propose two SU F(2) functions, namely,collaborative selection (F2.1) and determination of SU’s chan-nel access time (F2.2) in order to provide dedicated channelaccess to SUs (A2.1) in distributed (C1.2) SU networks. Thereare two types ofmarkets, namely, primarymarket (comprisedof SU service providers and PUs) and secondary market(comprised of SU service providers and SU hosts). The func-tionalities are modeled and solved using a hierarchical gametheoretic framework comprised of upper- and lower-levelgames and in a non-intracooperative (C2.2) mode and non-intercooperative (C3.2) mode, respectively. The purpose is tomaximize the SUs’ service provider and SU network utilityfunctions, 𝑈𝑝,𝑖(𝑡) and 𝑈𝑠,𝑖(𝑡), respectively. The hierarchicalgame theoretic framework is as follows.

(i) Secondary market allows SU hosts to purchase whitespaces from SU service providers on a short-termbasis (e.g., minutes), and it is a lower-level gamemodeled by evolutionary game. Each SU serviceprovider 𝑖 offers white spaces, which are representedby bandwidth 𝑏𝑖 and price 𝑝𝑖. Note that higher price𝑝𝑖 for a particular bandwidth 𝑏𝑖 reduces demandlevels, and so it improves network performance.Subsequently, each SU host competes and selects a SUservice provider. Hence, the secondary market imple-ments collaborator selection (F2.1). Each SU aims tomaximize its individual utility function defined as

𝑈𝑠,𝑖 (𝑡) = 𝛼 ⋅𝑏𝑖 (𝑡)

𝑝𝑖, (24)

where 𝛼 is a constant based on network performancerequirement, in order to maximize its network per-formance satisfaction.The number of SUs that chooseservice provider 𝑖 is represented by 𝑛𝑖(𝑡).

(ii) Primary market allows SU service providers to pur-chase white spaces from PUs (or spectrum brokers)on a long-term basis (e.g., weeks or months), and itis a upper-level game modeled by differential game.Each SU service provider 𝑖 purchases some amountof white spaces 𝑐𝑖(𝑡) from PUs based on the selectionof SU service providers 𝑥𝑖(𝑡) in order to maximizeprofits. Hence, it implements the determination ofSU’s channel access time (F2.2). Note that higheramount of the purchased white spaces improvesnetwork performance and so it attracts more SUs;however, it reduces monetary revenues. Each SUservice provider 𝑖 adjusts the amount of white spaces𝑐𝑖(𝑡), and maximizes its profit defined as

𝑈𝑝,𝑖 (𝑡) = 𝑝𝑖 ⋅ 𝑛𝑖 (𝑡) − 𝛽𝑖 ⋅ 𝑐2

𝑖(𝑡) , (25)

where 𝑝𝑖 ⋅ 𝑛𝑖(𝑡) represents the monetary revenue,𝛽𝑖 ⋅ 𝑐2

𝑖(𝑡) represents the cost paid to the PUs, and 𝛽𝑖

is a constant weight. Note that, with 𝑐2𝑖(𝑡), it causes

the cost to increase rapidly, and so it prevents aSU service provider 𝑖 from being too aggressive. AtNash equilibrium, each SU service provider obtainsmaximized profit. In differential game, the SU serviceproviders make decision simultaneously; however,some providers may make decision first, and theyare called the leaders. In this case, a Stackelbergdifferential game can be applied to achieve Stack-elberg equilibrium. In Stackelberg game, the leaderproviders make decisions first, followed by followerproviders. So, the leader providers can achieve higherpay-off, and the follower providers make decisionbased on the optimal strategies made by the leaderproviders. The spectrum leasing scheme has beenshown to increase SU service providers’ profits.